Instructions to use ModelsLab/unicontrol-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ModelsLab/unicontrol-v1.1 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ModelsLab/unicontrol-v1.1", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("ModelsLab/unicontrol-v1.1", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]Stable Diffusion UniControl 1.1 Model Card
Stable Diffusion UniControl is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input.
You can use this with the 🧨Diffusers Plus Plus library., our fork of diffusers.
Note: For now, please install diffusers_plus_plus from github. Specifically the unicontrol branch to access UniControl Pipeline
Original GitHub Repository
- Original Code from authors here.
Model Details
Developed by: Qin, Can and Zhang, Shu and Yu, Ning and Feng, Yihao and Yang, Xinyi and Zhou, Yingbo and Wang, Huan and Niebles, Juan Carlos and Xiong, Caiming and Savarese, Silvio and others
Model type: Diffusion-based controlnet for text-to-image generation model with multiple conditionings
Language(s): English
Model Description: This is a model that can be used to generate and modify images based on text prompts and a condition image. It is a Latent Diffusion Model that uses a fixed, pretrained text encoder (CLIP ViT-L/14) as suggested in the Imagen paper.
Resources for more information: GitHub Repository, Paper.
Cite as:
@article{qin2023unicontrol, title={UniControl: A Unified Diffusion Model for Controllable Visual Generation In the Wild}, author={Qin, Can and Zhang, Shu and Yu, Ning and Feng, Yihao and Yang, Xinyi and Zhou, Yingbo and Wang, Huan and Niebles, Juan Carlos and Xiong, Caiming and Savarese, Silvio and others}, journal={arXiv preprint arXiv:2305.11147}, year={2023} }
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